Triple loss-based improved neural network pedestrian re-identification method

Abstract

The invention discloses a triple loss-based improved neural network pedestrian re-identification method. The method comprises the following steps of constructing a sample database, establishing positive and negative sample libraries based on the sample database, and randomly selecting two positive samples and one negative sample to form a triple; constructing a triple loss-based neural network, and performing training, wherein the neural network is formed by connecting three parallel convolution neural networks with a triple loss layer; inputting a to-be-tested picture and each sample picture in the expanded sample database, which serve as a group of inputs, to the trained neural network in sequence, wherein another input of the neural network is zero or zero input; and calculating a distance of eigenvectors of two input pictures output by the neural network by utilizing a Euclidean distance, and querying and arranging first 20 Euclidean distances in an ascending order, and then performing simple manual screening to obtain a final identification result. The method has the beneficial effects that the identification method can be suitable for a picture scene with a relatively great change, can ensure robustness, and has relatively high identification accuracy.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an improved neural network pedestrian re-identification method based on triple loss. Background technique [0002] With the advancement of technology, smart devices such as computers are more and more widely used in people's daily life. Computers are more efficient and accurate than humans in handling repetitive and data-intensive tasks. Naturally, people hope that computers can deal with some more intelligent problems like humans. Computer vision is an important part in the new application field of computer. It is the core and the most extensive application of computer vision to replace or assist humans to complete the detection and tracking of targets. From fingerprints or faces used in daily life From unlocking, to automatic driving of cars, robot control, etc. are all closely related to computer vision technology. Human beings are the main body of social life, and...

AI Technical Summary

Problems solved by technology

However, due to the changeable posture of the human body, the shape is not fixed, and the color characteristics are different with the change of clothing, it is still a very challenging subject. However, due to its wide application prospects, although the task of pedestrian detection is facing many difficulties, still attracts the attention of many researchers

[0003] Most of the current pedestrian recognition methods use softmax regression to converge the convolutional neural network and generate a model for people classification. However, when the picture scene changes greatly, the robustness cannot be guaranteed, and it cannot be applied to multiple scenes (ie Figures with a fixed background, poor portability)

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